Mining adversarial patterns via regularized loss minimization

Machine Learning

Volumn 81, Issue 1, 2010, Pages 69-83

  Liu, Wei ^a   Chawla, Sanjay ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Loss minimization; Nash equilibrium; Stackelberg game

Indexed keywords

CLASSIFICATION METHODS; DATA MINERS; DATA TRANSFORMATION; ERROR RATE; LOSS FUNCTIONS; LOSS MINIMIZATION; NASH EQUILIBRIUM; SPAM EMAILS; SPAM FILTER; STACKELBERG GAMES; SYNTHETIC AND REAL DATA; TEST DATA; TEST SETS; UNDERLYING DISTRIBUTION;

CLASSIFIERS; FILTRATION; INTERNET; MINERS;

OPTIMIZATION;

EID: 77955660961     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-010-5199-2     Document Type: Conference Paper

References (20)

1. M. Branch T. Coleman Y. Li 2000 A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems SIAM Journal on Scientific Computing 21 1 1 23 10.1137/S1064827595289108 1722103
2. R. Byrd R. Schnabel G. Shultz 1988 Approximate solution of the trust region problem by minimization over two-dimensional subspaces Mathematical Programming 40 1 247 263 0652.90082 10.1007/BF01580735 941311
3. M. Collins R. Schapire Y. Singer 2002 Logistic regression, AdaBoost and Bregman distances Machine Learning 48 1 253 285 0998.68123 10.1023/A: 1013912006537 (Pubitemid 34247580)
4. Dalvi, N., Domingos, P. Mausam, Sanghai, S., & Verma, D. (2004). Adversarial classification. In KDD'04: proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 99-108). New York: ACM.
5. S. J. Delany P. Cunningham A. Tsymbal L. Coyle 2005 A case-based technique for tracking concept drift in spam filtering Knowledge-Based Systems 18 4-5 187 195 10.1016/j.knosys.2004.10.002 (Pubitemid 41013901)
6. J. Demšar 2006 Statistical comparisons of classifiers over multiple data sets The Journal of Machine Learning Research 7 30
7. Dixit, A., & Skeath, S. (1999). Games of strategy. New York: Norton.
8. Fudenberg, D., & Tirole, J. (1991). Game theory (1st ed.). Cambridge: MIT Press.
9. Globerson, A., & Roweis, S. (2006). Nightmare at test time: robust learning by feature deletion. In Proceedings of the 23rd international conference on machine learning (pp. 353-360). New York: ACM. (Pubitemid 46966864)
10. Globerson, A., Teo, C. H., Smola, A., & Roweis, S. (2008). An adversarial view of covariate shift and a minimax approach. In Dataset shift in machine learning. Cambridge: MIT Press.
11. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning.
12. Kantarcioglu, M., Xi, B., & Clifton, C. (2009). Classifier evaluation and attribute selection against active adversaries (Technical report). Department of Statistics, Purdue University.
13. S. Keerthi D. DeCoste 2006 A modified finite Newton method for fast solution of large scale linear SVMs Journal of Machine Learning Research 6 1 341 2249824
14. Kołcz, A., & Teo, C. (2009). Feature weighting for improved classifier robustness. In CEAS'09: sixth conference on email and anti-spam.
15. C. Lin R. Weng S. Keerthi 2008 Trust region Newton method for logistic regression The Journal of Machine Learning Research 9 627 650 2417250
16. Liu, W., & Chawla, S. (2009). A game theoretical model for adversarial learning. In Proceedings of the 2009 IEEE international conference on data mining workshops (pp. 25-30). Los Alamitos: IEEE Comput. Soc.
17. Lowd, D., & Meek, C. (2005). Adversarial learning. In KDD'05: proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 641-647). New York: ACM. (Pubitemid 43218333)
18. J. Moré D. Sorensen 1983 Computing a trust region step SIAM Journal on Scientific and Statistical Computing 4 553 0551.65042 10.1137/0904038
19. T. Steihaug 1983 The conjugate gradient method and trust regions in large scale optimization SIAM Journal on Numerical Analysis 20 3 626 637 0518.65042 10.1137/0720042 701102
20. I. Witten E. Frank 2002 Data mining: practical machine learning tools and techniques with Java implementations ACM SIGMOD Record 31 1 76 77 10.1145/507338.507355